A Health state-related ensemble deep learning method for aircraft engine remaining useful life prediction

预言 自编码 计算机科学 深度学习 马氏距离 人工智能 集成学习 集合预报 机器学习 卷积神经网络 数据挖掘
作者
Yujie Cheng,Jiyan Zeng,Zili Wang,Dengwei Song
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:135: 110041-110041 被引量:15
标识
DOI:10.1016/j.asoc.2023.110041
摘要

Remaining useful life (RUL) prediction for aircraft engines is crucial to enabling predictive maintenance. Current RUL predictions for aircraft engines mainly focus on model-based and data-driven methods that employ a single model or algorithm. Few studies on RUL prediction have been conducted by using an ensemble method that combines prediction results from multiple algorithms. As an emerging frontier technology, ensemble learning has become a topic of interest in the field of RUL prediction because it can achieve better prediction performance than single model. In this study, a health-state-related (HSR) ensemble deep learning method that considers different degradation laws of the aircraft engine is proposed for RUL prediction. First, a health baseline is constructed and lifetime degradation is divided into several health states to represent different degradation laws. The Mahalanobis distance to the health baseline is utilized to recognize the current health state of the aircraft engine. Second, three deep learning methods, namely stacked autoencoder, convolutional neural network and long short-term memory, are selected as member algorithms and trained on different health states. Thus, different member algorithm sets are constructed for different health states, learning different degradation laws in different health states. Third, self-adaptive ensemble weight sets for different health states are calculated by applying ridge regression, which can comprehensively utilize the prediction results of each algorithm model in different health states. A case study is conducted by using a dataset of the PHM data challenge to demonstrate the effectiveness of the proposed method. The experiment result shows that the proposed HSR ensemble deep learning method can considerably improve prediction performance compared with methods that are based on a single prediction algorithm and ensemble learning method that does not consider the health state.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
11发布了新的文献求助10
1秒前
1秒前
2秒前
不学无术发布了新的文献求助10
4秒前
hanna完成签到 ,获得积分10
4秒前
5秒前
科研通AI5应助科研通管家采纳,获得10
5秒前
斯文败类应助科研通管家采纳,获得10
5秒前
小二郎应助科研通管家采纳,获得10
6秒前
领导范儿应助科研通管家采纳,获得10
6秒前
Orange应助科研通管家采纳,获得10
6秒前
香蕉觅云应助科研通管家采纳,获得10
6秒前
FashionBoy应助科研通管家采纳,获得10
6秒前
桐桐应助科研通管家采纳,获得10
6秒前
彭于晏应助科研通管家采纳,获得10
6秒前
慕青应助科研通管家采纳,获得10
6秒前
科研通AI5应助科研通管家采纳,获得10
6秒前
爆米花应助科研通管家采纳,获得10
6秒前
czh应助科研通管家采纳,获得20
6秒前
qqshown完成签到,获得积分10
7秒前
7秒前
7秒前
7秒前
7秒前
9秒前
量子星尘发布了新的文献求助10
9秒前
XAN发布了新的文献求助10
10秒前
DijiaXu应助必过六级采纳,获得10
12秒前
小门发布了新的文献求助10
12秒前
13秒前
Afaq发布了新的文献求助10
13秒前
马er发布了新的文献求助10
15秒前
16秒前
LIU完成签到 ,获得积分10
16秒前
16秒前
ing完成签到,获得积分10
18秒前
18秒前
19秒前
SciGPT应助Quinna采纳,获得10
20秒前
21秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136